Autonomous construction of a WiFi access point map using multidimensional scaling

Jahyoung Koo; Hojung Cha

doi:10.1007/978-3-642-21726-5_8

Autonomous construction of a WiFi access point map using multidimensional scaling

Jahyoung Koo
, Hojung Cha

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Citations (Scopus)

Abstract

To construct a WiFi positioning system, dedicated individuals usually gather radio scans with ground truth data. This laborious operation limits the widespread use of WiFi-based locating system. Off-the-shelf smartphones have the capability to scan radio signals from WiFi Access Points (APs). In this paper we propose a scheme to construct a map of WiFi AP positions autonomously without ground truth information. From radio scans, we extract dissimilarities between pairs of WiFi APs, then analyze the dissimilarities to produce a geometric configuration of WiFi APs based on a multidimensional scaling technique. To validate our scheme, we conducted experiments on five floors of an office building that has an area of 50 m by 35 m in each floor. WiFi APs were located within a 10m error range, and floors of APs are recognized without error.

Original language	English
Title of host publication	Pervasive Computing - 9th International Conference, Pervasive 2011, Proceedings
Pages	115-132
Number of pages	18
DOIs	https://doi.org/10.1007/978-3-642-21726-5_8
Publication status	Published - 2011
Event	9th International Conference on Pervasive Computing, Pervasive 2011 - San Francisco, CA, United States Duration: 2011 Jun 12 → 2011 Jun 15

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6696 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Pervasive Computing, Pervasive 2011
Country/Territory	United States
City	San Francisco, CA
Period	11/6/12 → 11/6/15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0000405).

Funding Information:
Acknowledgments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0000405).

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-21726-5_8

Cite this

Koo, J., & Cha, H. (2011). Autonomous construction of a WiFi access point map using multidimensional scaling. In Pervasive Computing - 9th International Conference, Pervasive 2011, Proceedings (pp. 115-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6696 LNCS). https://doi.org/10.1007/978-3-642-21726-5_8

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title = "Autonomous construction of a WiFi access point map using multidimensional scaling",

abstract = "To construct a WiFi positioning system, dedicated individuals usually gather radio scans with ground truth data. This laborious operation limits the widespread use of WiFi-based locating system. Off-the-shelf smartphones have the capability to scan radio signals from WiFi Access Points (APs). In this paper we propose a scheme to construct a map of WiFi AP positions autonomously without ground truth information. From radio scans, we extract dissimilarities between pairs of WiFi APs, then analyze the dissimilarities to produce a geometric configuration of WiFi APs based on a multidimensional scaling technique. To validate our scheme, we conducted experiments on five floors of an office building that has an area of 50 m by 35 m in each floor. WiFi APs were located within a 10m error range, and floors of APs are recognized without error.",

keywords = "Autonomous and Unsupervised Learning, Multidimensional Scaling, Positioning, WiFi Access Point Map",

author = "Jahyoung Koo and Hojung Cha",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0000405). Funding Information: Acknowledgments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0000405).; 9th International Conference on Pervasive Computing, Pervasive 2011 ; Conference date: 12-06-2011 Through 15-06-2011",

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Koo, J & Cha, H 2011, Autonomous construction of a WiFi access point map using multidimensional scaling. in Pervasive Computing - 9th International Conference, Pervasive 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6696 LNCS, pp. 115-132, 9th International Conference on Pervasive Computing, Pervasive 2011, San Francisco, CA, United States, 11/6/12. https://doi.org/10.1007/978-3-642-21726-5_8

Autonomous construction of a WiFi access point map using multidimensional scaling. / Koo, Jahyoung; Cha, Hojung.
Pervasive Computing - 9th International Conference, Pervasive 2011, Proceedings. 2011. p. 115-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6696 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0000405). Funding Information: Acknowledgments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0000405).

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N2 - To construct a WiFi positioning system, dedicated individuals usually gather radio scans with ground truth data. This laborious operation limits the widespread use of WiFi-based locating system. Off-the-shelf smartphones have the capability to scan radio signals from WiFi Access Points (APs). In this paper we propose a scheme to construct a map of WiFi AP positions autonomously without ground truth information. From radio scans, we extract dissimilarities between pairs of WiFi APs, then analyze the dissimilarities to produce a geometric configuration of WiFi APs based on a multidimensional scaling technique. To validate our scheme, we conducted experiments on five floors of an office building that has an area of 50 m by 35 m in each floor. WiFi APs were located within a 10m error range, and floors of APs are recognized without error.

AB - To construct a WiFi positioning system, dedicated individuals usually gather radio scans with ground truth data. This laborious operation limits the widespread use of WiFi-based locating system. Off-the-shelf smartphones have the capability to scan radio signals from WiFi Access Points (APs). In this paper we propose a scheme to construct a map of WiFi AP positions autonomously without ground truth information. From radio scans, we extract dissimilarities between pairs of WiFi APs, then analyze the dissimilarities to produce a geometric configuration of WiFi APs based on a multidimensional scaling technique. To validate our scheme, we conducted experiments on five floors of an office building that has an area of 50 m by 35 m in each floor. WiFi APs were located within a 10m error range, and floors of APs are recognized without error.

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KW - Positioning

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Autonomous construction of a WiFi access point map using multidimensional scaling

Abstract

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